Overview

Dataset statistics

Number of variables11
Number of observations314835
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.8 MiB
Average record size in memory96.0 B

Variable types

Categorical2
Numeric9

Alerts

Parqty_Adj is highly overall correlated with Parqty_Cal and 1 other fieldsHigh correlation
Parqty_Cal is highly overall correlated with Parqty_Adj and 2 other fieldsHigh correlation
parqty is highly overall correlated with Parqty_Cal and 1 other fieldsHigh correlation
pr_dpcode is highly overall correlated with pr_level1_keyHigh correlation
pr_level1_key is highly overall correlated with pr_dpcode and 1 other fieldsHigh correlation
sd_t_qty_30 is highly overall correlated with Parqty_Adj and 2 other fieldsHigh correlation
sh_daysmin is highly overall correlated with pr_level1_keyHigh correlation
parqty is highly skewed (γ1 = 40.54068451)Skewed
sd_t_qty_30 is highly skewed (γ1 = 42.63578981)Skewed
Parqty_Cal is highly skewed (γ1 = 40.44174072)Skewed
Parqty_Adj is highly skewed (γ1 = 44.27509029)Skewed
sd_t_qty_30 has 72450 (23.0%) zerosZeros
Parqty_Cal has 74129 (23.5%) zerosZeros

Reproduction

Analysis started2024-02-14 07:21:36.906880
Analysis finished2024-02-14 07:22:05.771473
Duration28.86 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

pr_level1_key
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
1
261033 
2
53802 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314835
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 261033
82.9%
2 53802
 
17.1%

Length

2024-02-14T07:22:05.933551image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-14T07:22:06.115996image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1 261033
82.9%
2 53802
 
17.1%

Most occurring characters

ValueCountFrequency (%)
1 261033
82.9%
2 53802
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314835
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 261033
82.9%
2 53802
 
17.1%

Most occurring scripts

ValueCountFrequency (%)
Common 314835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 261033
82.9%
2 53802
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 261033
82.9%
2 53802
 
17.1%

pr_dpcode
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.47961
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2024-02-14T07:22:06.288910image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q319
95-th percentile28
Maximum28
Range27
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9226574
Coefficient of variation (CV)0.77726137
Kurtosis-1.1214515
Mean11.47961
Median Absolute Deviation (MAD)7
Skewness0.49317313
Sum3614183
Variance79.613815
MonotonicityNot monotonic
2024-02-14T07:22:06.526576image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 38090
12.1%
19 37113
11.8%
8 33647
10.7%
2 32641
10.4%
28 28835
9.2%
9 26528
8.4%
5 22240
7.1%
10 17178
 
5.5%
23 15690
 
5.0%
3 14355
 
4.6%
Other values (13) 48518
15.4%
ValueCountFrequency (%)
1 38090
12.1%
2 32641
10.4%
3 14355
 
4.6%
4 2795
 
0.9%
5 22240
7.1%
6 6841
 
2.2%
7 69
 
< 0.1%
8 33647
10.7%
9 26528
8.4%
10 17178
5.5%
ValueCountFrequency (%)
28 28835
9.2%
27 1774
 
0.6%
25 1495
 
0.5%
23 15690
5.0%
22 5811
 
1.8%
21 7120
 
2.3%
20 11920
 
3.8%
19 37113
11.8%
18 1508
 
0.5%
17 30
 
< 0.1%

iprcode
Real number (ℝ)

Distinct18126
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200488.52
Minimum26
Maximum260717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2024-02-14T07:22:06.760194image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile36098
Q1181324
median230433
Q3250537
95-th percentile258501
Maximum260717
Range260691
Interquartile range (IQR)69213

Descriptive statistics

Standard deviation70572.811
Coefficient of variation (CV)0.35200424
Kurtosis0.88233033
Mean200488.52
Median Absolute Deviation (MAD)23685
Skewness-1.4338677
Sum6.3120805 × 1010
Variance4.9805217 × 109
MonotonicityNot monotonic
2024-02-14T07:22:07.009701image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
249816 33
 
< 0.1%
235945 33
 
< 0.1%
242226 33
 
< 0.1%
193015 32
 
< 0.1%
242232 32
 
< 0.1%
111150 32
 
< 0.1%
231714 32
 
< 0.1%
258407 32
 
< 0.1%
239911 32
 
< 0.1%
112568 32
 
< 0.1%
Other values (18116) 314512
99.9%
ValueCountFrequency (%)
26 29
< 0.1%
33 18
< 0.1%
38 25
< 0.1%
40 24
< 0.1%
47 25
< 0.1%
48 21
< 0.1%
85 8
 
< 0.1%
86 14
< 0.1%
99 24
< 0.1%
100 7
 
< 0.1%
ValueCountFrequency (%)
260717 4
 
< 0.1%
260716 2
 
< 0.1%
260710 11
< 0.1%
260681 7
 
< 0.1%
260593 13
< 0.1%
260592 20
< 0.1%
260591 17
< 0.1%
260583 23
< 0.1%
260574 1
 
< 0.1%
260573 3
 
< 0.1%

pack_size
Real number (ℝ)

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.905001
Minimum0
Maximum250
Zeros126
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2024-02-14T07:22:07.250626image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile36
Maximum250
Range250
Interquartile range (IQR)12

Descriptive statistics

Standard deviation14.16099
Coefficient of variation (CV)1.0184099
Kurtosis34.183986
Mean13.905001
Median Absolute Deviation (MAD)6
Skewness4.2063064
Sum4377781
Variance200.53365
MonotonicityNot monotonic
2024-02-14T07:22:07.504413image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 104135
33.1%
1 52555
16.7%
24 37345
 
11.9%
6 33630
 
10.7%
10 14550
 
4.6%
20 10626
 
3.4%
8 9269
 
2.9%
36 6226
 
2.0%
48 5895
 
1.9%
4 4961
 
1.6%
Other values (45) 35643
 
11.3%
ValueCountFrequency (%)
0 126
 
< 0.1%
1 52555
16.7%
2 84
 
< 0.1%
3 2783
 
0.9%
4 4961
 
1.6%
5 1605
 
0.5%
6 33630
10.7%
7 287
 
0.1%
8 9269
 
2.9%
9 2379
 
0.8%
ValueCountFrequency (%)
250 31
 
< 0.1%
240 19
 
< 0.1%
200 22
 
< 0.1%
180 22
 
< 0.1%
160 21
 
< 0.1%
150 62
 
< 0.1%
144 376
0.1%
132 18
 
< 0.1%
125 48
 
< 0.1%
120 358
0.1%

brcode
Real number (ℝ)

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1029.0129
Minimum1000
Maximum1053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2024-02-14T07:22:07.738335image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1003
Q11015
median1030
Q31044
95-th percentile1052
Maximum1053
Range53
Interquartile range (IQR)29

Descriptive statistics

Standard deviation15.970025
Coefficient of variation (CV)0.015519753
Kurtosis-1.2349243
Mean1029.0129
Median Absolute Deviation (MAD)14
Skewness-0.15314175
Sum3.2396926 × 108
Variance255.04171
MonotonicityNot monotonic
2024-02-14T07:22:07.970478image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1000 10824
 
3.4%
1011 10687
 
3.4%
1049 10308
 
3.3%
1014 10116
 
3.2%
1030 10096
 
3.2%
1032 9782
 
3.1%
1021 9619
 
3.1%
1016 9592
 
3.0%
1048 9423
 
3.0%
1051 9285
 
2.9%
Other values (27) 215103
68.3%
ValueCountFrequency (%)
1000 10824
3.4%
1003 8851
2.8%
1005 9058
2.9%
1006 8183
2.6%
1007 7385
2.3%
1011 10687
3.4%
1012 5352
1.7%
1014 10116
3.2%
1015 9260
2.9%
1016 9592
3.0%
ValueCountFrequency (%)
1053 7144
2.3%
1052 9006
2.9%
1051 9285
2.9%
1050 8771
2.8%
1049 10308
3.3%
1048 9423
3.0%
1047 8951
2.8%
1046 8630
2.7%
1044 9238
2.9%
1042 8177
2.6%

parqty
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct243
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.536462
Minimum0
Maximum2200
Zeros371
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2024-02-14T07:22:08.205125image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median6
Q39
95-th percentile24
Maximum2200
Range2200
Interquartile range (IQR)5

Descriptive statistics

Standard deviation13.962058
Coefficient of variation (CV)1.635579
Kurtosis4619.8164
Mean8.536462
Median Absolute Deviation (MAD)2
Skewness40.540685
Sum2687577
Variance194.93906
MonotonicityNot monotonic
2024-02-14T07:22:08.444772image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 88125
28.0%
4 54549
17.3%
3 43537
13.8%
12 21252
 
6.8%
8 15491
 
4.9%
2 13926
 
4.4%
10 12650
 
4.0%
5 10934
 
3.5%
7 7583
 
2.4%
9 5728
 
1.8%
Other values (233) 41060
13.0%
ValueCountFrequency (%)
0 371
 
0.1%
1 660
 
0.2%
2 13926
 
4.4%
3 43537
13.8%
4 54549
17.3%
5 10934
 
3.5%
6 88125
28.0%
7 7583
 
2.4%
8 15491
 
4.9%
9 5728
 
1.8%
ValueCountFrequency (%)
2200 2
< 0.1%
1300 1
< 0.1%
1000 2
< 0.1%
960 1
< 0.1%
814 1
< 0.1%
751 1
< 0.1%
720 1
< 0.1%
700 1
< 0.1%
650 1
< 0.1%
648 1
< 0.1%

sh_daysmin
Real number (ℝ)

HIGH CORRELATION 

Distinct977
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441.29151
Minimum4
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2024-02-14T07:22:08.693329image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile32
Q1183
median329
Q3641
95-th percentile1095
Maximum9999
Range9995
Interquartile range (IQR)458

Descriptive statistics

Standard deviation475.48803
Coefficient of variation (CV)1.0774919
Kurtosis132.98503
Mean441.29151
Median Absolute Deviation (MAD)170
Skewness8.1042342
Sum1.3893401 × 108
Variance226088.87
MonotonicityNot monotonic
2024-02-14T07:22:09.214875image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 20391
 
6.5%
658 19430
 
6.2%
180 15439
 
4.9%
329 8619
 
2.7%
730 7437
 
2.4%
90 6668
 
2.1%
120 6264
 
2.0%
300 6007
 
1.9%
360 5989
 
1.9%
657 5690
 
1.8%
Other values (967) 212901
67.6%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 27
 
< 0.1%
6 14
 
< 0.1%
7 222
 
0.1%
8 376
0.1%
9 138
 
< 0.1%
10 617
0.2%
11 129
 
< 0.1%
12 636
0.2%
13 604
0.2%
ValueCountFrequency (%)
9999 237
0.1%
7200 20
 
< 0.1%
6570 27
 
< 0.1%
4601 15
 
< 0.1%
4250 24
 
< 0.1%
3650 246
0.1%
3600 162
0.1%
3550 24
 
< 0.1%
3500 8
 
< 0.1%
3470 102
< 0.1%

sd_t_qty_30
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1164
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3252265
Minimum0
Maximum3794
Zeros72450
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2024-02-14T07:22:09.451821image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile27
Maximum3794
Range3794
Interquartile range (IQR)6

Descriptive statistics

Standard deviation24.295039
Coefficient of variation (CV)3.3166264
Kurtosis4149.9813
Mean7.3252265
Median Absolute Deviation (MAD)3
Skewness42.63579
Sum2306237.7
Variance590.24894
MonotonicityNot monotonic
2024-02-14T07:22:09.705957image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72450
23.0%
1 44825
14.2%
2 34034
10.8%
3 26274
 
8.3%
4 20542
 
6.5%
5 15746
 
5.0%
6 13694
 
4.3%
7 10733
 
3.4%
8 8663
 
2.8%
9 7054
 
2.2%
Other values (1154) 60820
19.3%
ValueCountFrequency (%)
0 72450
23.0%
0.02 17
 
< 0.1%
0.03 3
 
< 0.1%
0.04 15
 
< 0.1%
0.05 8
 
< 0.1%
0.06 13
 
< 0.1%
0.07 2
 
< 0.1%
0.08 17
 
< 0.1%
0.09 5
 
< 0.1%
0.1 25
 
< 0.1%
ValueCountFrequency (%)
3794 1
< 0.1%
3150 1
< 0.1%
2609 1
< 0.1%
2367.5 1
< 0.1%
1748 1
< 0.1%
1624 1
< 0.1%
1556 1
< 0.1%
1499 1
< 0.1%
1396 1
< 0.1%
1314.5 1
< 0.1%

on_promotion
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
0.0
248629 
1.25
66206 

Length

Max length4
Median length3
Mean length3.2102879
Min length3

Characters and Unicode

Total characters1010711
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 248629
79.0%
1.25 66206
 
21.0%

Length

2024-02-14T07:22:09.940800image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-14T07:22:10.116219image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 248629
79.0%
1.25 66206
 
21.0%

Most occurring characters

ValueCountFrequency (%)
0 497258
49.2%
. 314835
31.1%
1 66206
 
6.6%
2 66206
 
6.6%
5 66206
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 695876
68.9%
Other Punctuation 314835
31.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 497258
71.5%
1 66206
 
9.5%
2 66206
 
9.5%
5 66206
 
9.5%
Other Punctuation
ValueCountFrequency (%)
. 314835
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1010711
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 497258
49.2%
. 314835
31.1%
1 66206
 
6.6%
2 66206
 
6.6%
5 66206
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1010711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 497258
49.2%
. 314835
31.1%
1 66206
 
6.6%
2 66206
 
6.6%
5 66206
 
6.6%

Parqty_Cal
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct411
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6461607
Minimum0
Maximum3161
Zeros74129
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2024-02-14T07:22:10.313259image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile25
Maximum3161
Range3161
Interquartile range (IQR)6

Descriptive statistics

Standard deviation20.071393
Coefficient of variation (CV)3.0199982
Kurtosis4067.4969
Mean6.6461607
Median Absolute Deviation (MAD)3
Skewness40.441741
Sum2092444
Variance402.86082
MonotonicityNot monotonic
2024-02-14T07:22:10.568824image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74129
23.5%
1 52722
16.7%
3 29214
 
9.3%
2 27277
 
8.7%
4 21077
 
6.7%
5 17207
 
5.5%
6 13305
 
4.2%
8 9225
 
2.9%
7 7912
 
2.5%
9 7747
 
2.5%
Other values (401) 55020
17.5%
ValueCountFrequency (%)
0 74129
23.5%
1 52722
16.7%
2 27277
 
8.7%
3 29214
 
9.3%
4 21077
 
6.7%
5 17207
 
5.5%
6 13305
 
4.2%
7 7912
 
2.5%
8 9225
 
2.9%
9 7747
 
2.5%
ValueCountFrequency (%)
3161 1
< 0.1%
2625 1
< 0.1%
2174 1
< 0.1%
1745 1
< 0.1%
1578 1
< 0.1%
1354 1
< 0.1%
1296 1
< 0.1%
1249 1
< 0.1%
1227 1
< 0.1%
1009 1
< 0.1%

Parqty_Adj
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct408
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5786714
Minimum3
Maximum3161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2024-02-14T07:22:10.817561image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q16
median6
Q38
95-th percentile25
Maximum3161
Range3158
Interquartile range (IQR)2

Descriptive statistics

Standard deviation19.410204
Coefficient of variation (CV)2.0263984
Kurtosis4624.2157
Mean9.5786714
Median Absolute Deviation (MAD)0
Skewness44.27509
Sum3015701
Variance376.75602
MonotonicityNot monotonic
2024-02-14T07:22:11.065018image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 159256
50.6%
8 44965
 
14.3%
3 34138
 
10.8%
7 7908
 
2.5%
9 7747
 
2.5%
10 6130
 
1.9%
11 5350
 
1.7%
4 4499
 
1.4%
13 4121
 
1.3%
14 3353
 
1.1%
Other values (398) 37368
 
11.9%
ValueCountFrequency (%)
3 34138
 
10.8%
4 4499
 
1.4%
5 1302
 
0.4%
6 159256
50.6%
7 7908
 
2.5%
8 44965
 
14.3%
9 7747
 
2.5%
10 6130
 
1.9%
11 5350
 
1.7%
12 2854
 
0.9%
ValueCountFrequency (%)
3161 1
< 0.1%
2625 1
< 0.1%
2174 1
< 0.1%
1745 1
< 0.1%
1578 1
< 0.1%
1354 1
< 0.1%
1296 1
< 0.1%
1249 1
< 0.1%
1227 1
< 0.1%
1009 1
< 0.1%

Interactions

2024-02-14T07:22:02.760910image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:48.060123image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:49.827053image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:51.578694image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:53.786593image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:55.539484image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:57.230421image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:58.988146image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:01.006218image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:02.949151image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:48.268662image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:50.018066image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:51.793073image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:53.983809image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:55.721482image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:57.418780image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:59.181973image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:01.196993image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:03.141308image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:48.464680image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:50.205141image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:52.021231image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:54.176149image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:55.910012image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:57.612499image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:59.379144image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:01.388486image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:03.331531image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:48.657648image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:50.395363image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:52.288213image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:54.366514image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:56.098655image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:57.805656image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:59.591089image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:01.580913image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:03.525831image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:48.853518image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:50.586100image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:52.553207image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:54.559630image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:56.283213image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:58.002146image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:59.791979image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:01.782780image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:03.708156image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:49.036470image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:50.766930image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:52.766349image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:54.744725image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:56.468798image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:58.184646image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:59.979062image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:01.974383image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:03.895760image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:49.225954image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:50.952529image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:53.003667image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:54.937761image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:56.658242image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:58.378597image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:00.171217image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:02.166004image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:04.094822image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:49.433466image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:51.156468image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:53.204815image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:55.139024image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:56.853274image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:58.595558image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:00.374409image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:02.364831image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:04.284901image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:49.636439image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:51.366851image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:53.410604image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:55.339525image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:57.041122image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:21:58.795057image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:00.801949image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:22:02.565138image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2024-02-14T07:22:11.253861image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Parqty_AdjParqty_Calbrcodeiprcodeon_promotionpack_sizeparqtypr_dpcodepr_level1_keysd_t_qty_30sh_daysmin
Parqty_Adj1.0000.637-0.060-0.1290.0180.3070.471-0.1860.0000.5570.164
Parqty_Cal0.6371.000-0.068-0.1890.0180.2400.5690.0400.0000.977-0.082
brcode-0.060-0.0681.000-0.0060.020-0.002-0.044-0.0000.010-0.068-0.006
iprcode-0.129-0.189-0.0061.0000.052-0.016-0.0950.0150.066-0.189-0.005
on_promotion0.0180.0180.0200.0521.0000.1610.118-0.0760.1050.1410.089
pack_size0.3070.240-0.002-0.0160.1611.0000.323-0.0110.1050.1700.069
parqty0.4710.569-0.044-0.0950.1180.3231.0000.0940.0030.546-0.042
pr_dpcode-0.1860.040-0.0000.015-0.076-0.0110.0941.0000.6900.071-0.281
pr_level1_key0.0000.0000.0100.0660.1050.1050.0030.6901.0000.117-0.506
sd_t_qty_300.5570.977-0.068-0.1890.1410.1700.5460.0710.1171.000-0.159
sh_daysmin0.164-0.082-0.006-0.0050.0890.069-0.042-0.281-0.506-0.1591.000

Missing values

2024-02-14T07:22:04.538958image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-14T07:22:05.065916image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

pr_level1_keypr_dpcodeiprcodepack_sizebrcodeparqtysh_daysminsd_t_qty_30on_promotionParqty_CalParqty_Adj
01321381910100743164.00.004.06
11522914212102443213.00.003.06
212170136361006113653.00.003.06
3112148421101233650.00.000.06
4121824422410461026317.00.0017.017
519258503241021614605.00.005.06
61102384149104863658.01.2510.010
7182160711441021736532.00.0032.032
8112484661101167306.00.004.04
9155669101011122470.00.000.06
pr_level1_keypr_dpcodeiprcodepack_sizebrcodeparqtysh_daysminsd_t_qty_30on_promotionParqty_CalParqty_Adj
3254591922834112103647672.00.02.06
3254601324096912102443000.00.00.06
325461112510301102337300.00.00.06
325462192021014100747300.00.00.06
325463112576631100037501.00.01.06
3254641325112912104942706.00.06.06
325465182470066101441801.00.01.06
325466187438121053126501.00.01.06
325467192045193105146583.00.03.06
3254682222367461101931202.00.02.03